38 Facts About Big data

Big data refers to data sets that are too large or complex to be dealt with by traditional data-processing application software.

Big data was originally associated with three key concepts: volume, variety, and velocity.

Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from big data, and seldom to a particular size of data set.

Size and number of available Big data sets have grown rapidly as Big data is collected by devices such as mobile devices, cheap and numerous information-sensing Internet of things devices, aerial, software logs, cameras, microphones, radio-frequency identification readers and wireless sensor networks.

Relational database management systems and desktop statistical software packages used to visualize data often have difficulty processing and analyzing big data.

Related searches

Term big data has been in use since the 1990s, with some giving credit to John Mashey for popularizing the term.

Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time.

Big data philosophy encompasses unstructured, semi-structured and structured data; however, the main focus is on unstructured data.

Big data "size" is a constantly moving target; as of 2012 ranging from a few dozen terabytes to many zettabytes of data.

Big data requires a set of techniques and technologies with new forms of integration to reveal insights from data-sets that are diverse, complex, and of a massive scale.

Big data repositories have existed in many forms, often built by corporations with a special need.

TeraBig data systems were the first to store and analyze 1 terabyte of Big data in 1992.

TeraBig data installed the first petabyte class RDBMS based system in 2007.

Since then, TeraBig data has added unstructured Big data types including XML, JSON, and Avro.

Multidimensional big data can be represented as OLAP data cubes or, mathematically, tensors.

Advancements in big data analysis offer cost-effective opportunities to improve decision-making in critical development areas such as health care, employment, economic productivity, crime, security, and natural disaster and resource management.

Additionally, user-generated Big data offers new opportunities to give the unheard a voice.

Big data analytics was used in healthcare by providing personalized medicine and prescriptive analytics, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries.

The level of Big data generated within healthcare systems is not trivial.

Especially since 2015, big data has come to prominence within business operations as a tool to help employees work more efficiently and streamline the collection and distribution of information technology .

The use of big data to resolve IT and data collection issues within an enterprise is called IT operations analytics .

Big data can be used to improve training and understanding competitors, using sport sensors.

Significant applications of big data included minimising the spread of the virus, case identification and development of medical treatment.

Encrypted search and cluster formation in big data were demonstrated in March 2014 at the American Society of Engineering Education.

Big data sets come with algorithmic challenges that previously did not exist.

Related searches

Research question that is asked about big data sets is whether it is necessary to look at the full data to draw certain conclusions about the properties of the data or if is a sample is good enough.

The name big data itself contains a term related to size and this is an important characteristic of big data.

Big data can be broken down by various data point categories such as demographic, psychographic, behavioral, and transactional data.

Critiques of the big data paradigm come in two flavors: those that question the implications of the approach itself, and those that question the way it is currently done.

Mark Graham has leveled broad critiques at Chris Anderson's assertion that big data will spell the end of theory: focusing in particular on the notion that big data must always be contextualized in their social, economic, and political contexts.

Much in the same line, it has been pointed out that the decisions based on the analysis of big data are inevitably "informed by the world as it was in the past, or, at best, as it currently is".

Large Big data sets have been analyzed by computing machines for well over a century, including the US census analytics performed by IBM's punch-card machines which computed statistics including means and variances of populations across the whole continent.

However, science experiments have tended to analyze their Big data using specialized custom-built high-performance computing clusters and grids, rather than clouds of cheap commodity computers as in the current commercial wave, implying a difference in both culture and technology stack.

Users of big data are often "lost in the sheer volume of numbers", and "working with Big Data is still subjective, and what it quantifies does not necessarily have a closer claim on objective truth".

Big data analysis is often shallow compared to analysis of smaller data sets.

Big data is a buzzword and a "vague term", but at the same time an "obsession" with entrepreneurs, consultants, scientists, and the media.

Big data has been used in policing and surveillance by institutions like law enforcement and corporations.

Conscientious usage of big data policing could prevent individual level biases from becoming institutional biases, Brayne notes.